Meiofauna of the profundal zone of the northern part of Lake Ladoga as an indicator of pollution

Benthic meiofauna was sampled at 19 stations, mainly in the northern part of Lake Ladoga, from depths between 13 and 199 m and from types of environment ranging from sheltered areas near pollution sources to less polluted open areas. About 80 taxa were identified, of these 70 to the species level. The greatest numbers of species were oligochaetes (24 species) and harpacticoids (8 species). Certain quantitative ratios of meiofauna were shown to be correlated with environmental data. The species of the oligochaete families Lumbriculidae and Aeolosomatidae and the harpacticoids as a collective group, excluding Canthocamptus staphylinus, were most clearly confined to the less eutrophied environments. The oligochaete species Amphichaeta leydigii, Dero digitata and Tubifex tubifex, the resting stages of Cyclopinae, and Eucyclops serrulatus among the Eucyclopinae were most clearly concentrated in the eutrophicated or polluted environments. The groups of Naididae, Cladocera and Eucyclopinae did not have much value as indicators. Shannon diversity index correlated positively with the total phosphorus content of the water, and number of species decreased with increasing depth. In general, it seems that the pollution tolerance of the meiofauna in a very large lake such as Lake Ladoga is high, presumably due to the effective mixing of water masses.     

Phenotypic Correlation of Juvenile Growth Rate between Different Consecutive foraging Environments in a Salmonid fish: A Field Experiment

Many organisms occupy considerably different environments during individual's lifespan. We are interested in how the phenotypic characteristics that are favourable in the earlier environment predict fitness in the later environment. High predictability of fitness between the two consecutive environments suggests that the either the same traits are favored in both environments, or that the favourable traits are genetically correlated. In this study, we ask how similarity of consecutive foraging environments affects the phenotypic correlation of juvenile brown trout growth rate. More specifically, we used a genetically narrow stock of hatchery-bred fish to contrast individual growth rates between high and low density hatchery environments, and thereafter between hatchery and natural lake environment. As expected, growth rate was highly dependent on the environment, and the fish showed considerable phenotypic plasticity. Furthermore, we found a strong positive correlation in growth rate between similar foraging environments, for example, between high and low density hatchery stocks, and between hatchery and a lake with small fish as main prey. However, hatchery growth did not predict growth rate in lakes where fish had to forage on bottom-dwelling invertebrates. Our results suggest that when the consecutive environments differed dramatically with respect to traits that fish use for foraging, relative performance of individual fish changed, earlier performance not being an accurate predictor of performance in the new environment. In this case, fitness of the fish was determined by an environment-specific set of traits that were not the same between the two consecutive environments. The result indicates that assessment of individual performance may be highly environment specific in trout. This revised version was published online in July 2006 with corrections to the Cover Date.     

Amazonian biodiversity and protected areas: do they meet?

Protected areas are crucial for Amazonian nature conservation. Many Amazonian reserves have been selected systematically to achieve biodiversity representativeness. We review the role natural-scientific understanding has played in reserve selection, and evaluate the theoretical potential of the existing reserves to cover a complete sample of the species diversity of the Amazonian rainforest biome. In total, 108 reserves (604,832 km²) are treated as strictly protected and Amazonian; 87 of these can be seen as systematically selected to sample species diversity (75.3% of total area). Because direct knowledge on all species distributions is unavailable, surrogates have been used to select reserves: direct information on some species distributions (15 reserves, 14.8% of total area); species distribution patterns predicted on the basis of conceptual models, mainly the Pleistocene refuge hypothesis, (5/10.3%); environmental units (46/27.3%); or a combination of distribution patterns and environmental units (21/22.9%). None of these surrogates are reliable: direct information on species distributions is inadequate; the Pleistocene refuge hypothesis is highly controversial; and environmental classifications do not capture all relevant ecological variation, and their relevance for species distribution patterns is undocumented. Hence, Amazonian reserves cannot be safely assumed to capture all Amazonian species. To improve the situation, transparency and an active dialogue with the scientific community should be integral to conservation planning. We suggest that the best currently available approach for sampling Amazonian species diversity in reserve selection is to simultaneously inventory indicator plant species and climatic and geological conditions, and to combine field studies with remote sensing.     

Scent of a killer: How could killer yeast boost its dispersal?

Vector‐borne parasites often manipulate hosts to attract uninfected vectors. For example, parasites causing malaria alter host odor to attract mosquitoes. Here, we discuss the ecology and evolution of fruit‐colonizing yeast in a tripartite symbiosis—the so‐called “killer yeast” system. “Killer yeast” consists of Saccharomyces cerevisiae yeast hosting two double‐stranded RNA viruses (M satellite dsRNAs, L‐A dsRNA helper virus). When both dsRNA viruses occur in a yeast cell, the yeast converts to lethal toxin‑producing “killer yeast” phenotype that kills uninfected yeasts. Yeasts on ephemeral fruits attract insect vectors to colonize new habitats. As the viruses have no extracellular stage, they depend on the same insect vectors as yeast for their dispersal. Viruses also benefit from yeast dispersal as this promotes yeast to reproduce sexually, which is how viruses can transmit to uninfected yeast strains. We tested whether insect vectors are more attracted to killer yeasts than to non‑killer yeasts. In our field experiment, we found that killer yeasts were more attractive to Drosophila than non‐killer yeasts. This suggests that vectors foraging on yeast are more likely to transmit yeast with a killer phenotype, allowing the viruses to colonize those uninfected yeast strains that engage in sexual reproduction with the killer yeast. Beyond insights into the basic ecology of the killer yeast system, our results suggest that viruses could increase transmission success by manipulating the insect vectors of their host.     

The use of spectral fluorescence methods to detect changes in the phytoplankton community

In vivo fluorescence methods are efficient toolsfor studying the seasonal and spatial dynamics ofphytoplankton. Traditionally the measurements are madeusing single excitation-emission wavelengthcombination. During a cruise in the Gulf of Riga(Baltic Sea) we supplemented this technique bymeasuring the spectral fluorescence signal (SFS) andfixed wavelength fluorescence intensities at theexcitation maxima of main accessory pigments. Thesemethods allowed the rapid collection of quantitativefluorescence data and chemotaxonomic diagnostics ofthe phytoplankton community. The chlorophylla-specific fluorescence intensities (R) and thespectral fluorescence fingerprints were analysedtogether with concentrations of chlorophyll a indifferent algal size-groups, phytoplankton biomass andtaxonomic position. The lower level of R in thesouthern gulf was related to the higher proportion ofcyanobacteria relative to total biomass and the lowerabundance of small algae. The phycoerythrinfluorescence signal was obviously due to the largecyanobacteria. The basin-wide shift in the shape ofchlorophyll a excitation spectra was caused bythe variable proportions of differently pigmentedcyanobacteria, diatoms and cryptomonads. This revised version was published online in July 2006 with corrections to the Cover Date.